US9971649B2 - Deploying and growing a set of dispersed storage units at and by non-information dispersal algorithm (IDA) width multiples - Google Patents

Abstract

Methods and apparatus for use in a dispersed storage network (DSN) to deploy and grow a set of dispersed storage (DS) units for use in the DSN memory. In an example of operation, a DS client module assigns one or more additional DS units to a storage set to form a new storage set, where data is encoded in the DSN utilizing a dispersed storage error encoding function in accordance with an information dispersal algorithm (IDA) width. For each encoded data slice stored in the existing storage set, the DS client module utilizes a distributed agreement protocol function to select a storage unit of the new storage set for storage of the encoded data slice.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/248,752, filed 30 Oct. 2015, entitled “MIGRATING DATA IN A DISPERSED STORAGE NETWORK,” which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks, and more particularly to cloud storage.

Description of Related Art

Computing devices are known to communicate data, process data, and/or store data. Such computing devices range from wireless smart phones, laptops, tablets, personal computers (PC), work stations, and video game devices, to data centers that support millions of web searches, stock trades, or on-line purchases every day. In general, a computing device includes a central processing unit (CPU), a memory system, user input/output interfaces, peripheral device interfaces, and an interconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using “cloud computing” to perform one or more computing functions (e.g., a service, an application, an algorithm, an arithmetic logic function, etc.) on behalf of the computer. Further, for large services, applications, and/or functions, cloud computing may be performed by multiple cloud computing resources in a distributed manner to improve the response time for completion of the service, application, and/or function. For example, Hadoop is an open source software framework that supports distributed applications enabling application execution by thousands of computers.

In addition to cloud computing, a computer may use “cloud storage” as part of its memory system. As is known, cloud storage enables a user, via its computer, to store files, applications, etc. on a remote storage system. The remote storage system may include a RAID (redundant array of independent disks) system and/or a dispersed storage system that uses an error correction scheme to encode data for storage.

In a RAID system, a RAID controller adds parity data to the original data before storing it across an array of disks. The parity data is calculated from the original data such that the failure of a single disk typically will not result in the loss of the original data. While RAID systems can address certain memory device failures, these systems may suffer from effectiveness, efficiency and security issues. For instance, as more disks are added to the array, the probability of a disk failure rises, which may increase maintenance costs. When a disk fails, for example, it needs to be manually replaced before another disk(s) fails and the data stored in the RAID system is lost. To reduce the risk of data loss, data on a RAID device is often copied to one or more other RAID devices. While this may reduce the possibility of data loss, it also raises security issues since multiple copies of data may be available, thereby increasing the chances of unauthorized access. In addition, co-location of some RAID devices may result in a risk of a complete data loss in the event of a natural disaster, fire, power surge/outage, etc.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, or distributed, storage network (DSN) in accordance with the present disclosure;

FIG. 2 is a schematic block diagram of an embodiment of a computing core in accordance with the present disclosure;

FIG. 3 is a schematic block diagram of an example of dispersed storage error encoding of data in accordance with the present disclosure;

FIG. 4 is a schematic block diagram of a generic example of an error encoding function in accordance with the present disclosure;

FIG. 5 is a schematic block diagram of a specific example of an error encoding function in accordance with the present disclosure;

FIG. 6 is a schematic block diagram of an example of slice naming information for an encoded data slice (EDS) in accordance with the present disclosure;

FIG. 7 is a schematic block diagram of an example of dispersed storage error decoding of data in accordance with the present disclosure;

FIG. 8 is a schematic block diagram of a generic example of an error decoding function in accordance with the present disclosure;

FIG. 9 is a schematic block diagram of an example of a dispersed storage network in accordance with the present disclosure;

FIG. 10A is a schematic block diagram of an embodiment of a decentralized agreement module in accordance with the present invention;

FIG. 10B is a flowchart illustrating an example of selecting the resource in accordance with the present invention;

FIG. 10C is a schematic block diagram of an embodiment of a dispersed storage network (DSN) in accordance with the present invention;

FIG. 10D is a flowchart illustrating an example of accessing a dispersed storage network (DSN) memory in accordance with the present invention;

FIG. 11A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) in accordance with the present invention; and

FIG. 11B is a flowchart illustrating an example of migrating data in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, or distributed, storage network (DSN)10 that includes a plurality of dispersed storage (DS) computing devices or processing units12-16, a managingunit18, anintegrity processing unit20, and aDSN memory22. The components of the DSN10 are coupled to anetwork24, which may include one or more wireless and/or wire lined communication systems; one or more non-public intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN).

The DSNmemory22 includes a plurality of dispersed storage units36 (DS units) that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.), at a common site, or a combination thereof. For example, if the DSNmemory22 includes eight dispersedstorage units36, each storage unit is located at a different site. As another example, if the DSNmemory22 includes eightstorage units36, all eight storage units are located at the same site. As yet another example, if the DSNmemory22 includes eightstorage units36, a first pair of storage units are at a first common site, a second pair of storage units are at a second common site, a third pair of storage units are at a third common site, and a fourth pair of storage units are at a fourth common site. Note that aDSN memory22 may include more or less than eightstorage units36.

Each of the DS computing devices12-16, the managingunit18, and theintegrity processing unit20 include acomputing core26, and network or communications interfaces30-33 which can be part of or external to computingcore26. DS computing devices12-16 may each be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment. Note that each of the managingunit18 and theintegrity processing unit20 may be separate computing devices, may be a common computing device, and/or may be integrated into one or more of the computing devices12-16 and/or into one or more of the dispersedstorage units36.

Eachinterface30,32, and33 includes software and hardware to support one or more communication links via thenetwork24 indirectly and/or directly. For example,interface30 supports a communication link (e.g., wired, wireless, direct, via a LAN, via thenetwork24, etc.) betweencomputing devices14 and16. As another example,interface32 supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network24) betweencomputing devices12 and16 and theDSN memory22. As yet another example,interface33 supports a communication link for each of the managingunit18 and theintegrity processing unit20 to thenetwork24.

Computing devices12 and16 include a dispersed storage (DS)client module34, which enables the computing device to dispersed storage error encode and decode data (e.g., data object40) as subsequently described with reference to one or more ofFIGS. 3-8. In this example embodiment,computing device16 functions as a dispersed storage processing agent for computingdevice14. In this role,computing device16 dispersed storage error encodes and decodes data on behalf ofcomputing device14. With the use of dispersed storage error encoding and decoding, theDSN10 is tolerant of a significant number of storage unit failures (the number of failures is based on parameters of the dispersed storage error encoding function) without loss of data and without the need for a redundant or backup copies of the data. Further, theDSN10 stores data for an indefinite period of time without data loss and in a secure manner (e.g., the system is very resistant to unauthorized attempts at accessing the data).

In operation, the managingunit18 performs DS management services. For example, the managingunit18 establishes distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for computing devices12-16 individually or as part of a group of user devices. As a specific example, the managingunit18 coordinates creation of a vault (e.g., a virtual memory block associated with a portion of an overall namespace of the DSN) within theDSN memory22 for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The managingunit18 facilitates storage of DS error encoding parameters for each vault by updating registry information of theDSN10, where the registry information may be stored in theDSN memory22, a computing device12-16, the managingunit18, and/or theintegrity processing unit20. The DS error encoding parameters (e.g., or dispersed storage error coding parameters) include data segmenting information (e.g., how many segments data (e.g., a file, a group of files, a data block, etc.) is divided into), segment security information (e.g., per segment encryption, compression, integrity checksum, etc.), error coding information (e.g., pillar width, decode threshold, read threshold, write threshold, etc.), slicing information (e.g., the number of encoded data slices that will be created for each data segment); and slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.).

The managingunit18 creates and stores user profile information (e.g., an access control list (ACL)) in local memory and/or within memory of theDSN memory22. The user profile information includes authentication information, permissions, and/or the security parameters. The security parameters may include encryption/decryption scheme, one or more encryption keys, key generation scheme, and/or data encoding/decoding scheme.

The managingunit18 creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the managingunit18 tracks the number of times a user accesses a non-public vault and/or public vaults, which can be used to generate per-access billing information. In another instance, the managingunit18 tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate per-data-amount billing information.

As another example, the managingunit18 performs network operations, network administration, and/or network maintenance. Network operations includes authenticating user data allocation requests (e.g., read and/or write requests), managing creation of vaults, establishing authentication credentials for user devices, adding/deleting components (e.g., user devices, storage units, and/or computing devices with a DS client module34) to/from theDSN10, and/or establishing authentication credentials for thestorage units36. Network operations can further include monitoring read, write and/or delete communications attempts, which attempts could be in the form of requests. Network administration includes monitoring devices and/or units for failures, maintaining vault information, determining device and/or unit activation status, determining device and/or unit loading, and/or determining any other system level operation that affects the performance level of theDSN10. Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of theDSN10.

To support data storage integrity verification within theDSN10, the integrity processing unit20 (and/or other devices in theDSN10 such as managing unit18) may assess and perform rebuilding of ‘bad’ or missing encoded data slices. At a high level, theintegrity processing unit20 performs rebuilding by periodically attempting to retrieve/list encoded data slices, and/or slice names of the encoded data slices, from theDSN memory22. Retrieved encoded slices are assessed and checked for errors due to data corruption, outdated versioning, etc. If a slice includes an error, it is flagged as a ‘bad’ or ‘corrupt’ slice. Encoded data slices that are not received and/or not listed may be flagged as missing slices. Bad and/or missing slices may be subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices in order to produce rebuilt slices. A multi-stage decoding process may be employed in certain circumstances to recover data even when the number of valid encoded data slices of a set of encoded data slices is less than a relevant decode threshold number. The rebuilt slices may then be written toDSN memory22. Note that theintegrity processing unit20 may be a separate unit as shown, included inDSN memory22, included in thecomputing device16, managingunit18, stored on aDS unit36, and/or distributed amongmultiple storage units36.

FIG. 2 is a schematic block diagram of an embodiment of acomputing core26 that includes aprocessing module50, amemory controller52,main memory54, a video graphics processing unit55, an input/output (IO)controller56, a peripheral component interconnect (PCI)interface58, anIO interface module60, at least one IOdevice interface module62, a read only memory (ROM) basic input output system (BIOS)64, and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module66, a host bus adapter (HBA)interface module68, anetwork interface module70, aflash interface module72, a harddrive interface module74, and aDSN interface module76.

TheDSN interface module76 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). TheDSN interface module76 and/or thenetwork interface module70 may function as one or more of the interface30-33 ofFIG. 1. Note that the IOdevice interface module62 and/or the memory interface modules66-76 may be collectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storage error encoding of data. When acomputing device12 or16 has data to store it disperse storage error encodes the data in accordance with a dispersed storage error encoding process based on dispersed storage error encoding parameters. The dispersed storage error encoding parameters include an encoding function (e.g., information dispersal algorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematic encoding, on-line codes, etc.), a data segmenting protocol (e.g., data segment size, fixed, variable, etc.), and per data segment encoding values. The per data segment encoding values include a total, or pillar width, number (T) of encoded data slices per encoding of a data segment (i.e., in a set of encoded data slices); a decode threshold number (D) of encoded data slices of a set of encoded data slices that are needed to recover the data segment; a read threshold number (R) of encoded data slices to indicate a number of encoded data slices per set to be read from storage for decoding of the data segment; and/or a write threshold number (W) to indicate a number of encoded data slices per set that must be accurately stored before the encoded data segment is deemed to have been properly stored. The dispersed storage error encoding parameters may further include slicing information (e.g., the number of encoded data slices that will be created for each data segment) and/or slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as the encoding function (a generic example is shown inFIG. 4 and a specific example is shown inFIG. 5); the data segmenting protocol is to divide the data object into fixed sized data segments; and the per data segment encoding values include: a pillar width of 5, a decode threshold of 3, a read threshold of 4, and a write threshold of 4. In accordance with the data segmenting protocol, thecomputing device12 or16 divides the data (e.g., a file (e.g., text, video, audio, etc.), a data object, or other data arrangement) into a plurality of fixed sized data segments (e.g., 1 through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more). The number of data segments created is dependent of the size of the data and the data segmenting protocol.

Thecomputing device12 or16 then disperse storage error encodes a data segment using the selected encoding function (e.g., Cauchy Reed-Solomon) to produce a set of encoded data slices.FIG. 4 illustrates a generic Cauchy Reed-Solomon encoding function, which includes an encoding matrix (EM), a data matrix (DM), and a coded matrix (CM). The size of the encoding matrix (EM) is dependent on the pillar width number (T) and the decode threshold number (D) of selected per data segment encoding values. To produce the data matrix (DM), the data segment is divided into a plurality of data blocks and the data blocks are arranged into D number of rows with Z data blocks per row. Note that Z is a function of the number of data blocks created from the data segment and the decode threshold number (D). The coded matrix is produced by matrix multiplying the data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encoding with a pillar number (T) of five and decode threshold number of three. In this example, a first data segment is divided into twelve data blocks (D1-D12). The coded matrix includes five rows of coded data blocks, where the first row of X11-X14 corresponds to a first encoded data slice (EDS 1_1), the second row of X21-X24 corresponds to a second encoded data slice (EDS 2_1), the third row of X31-X34 corresponds to a third encoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to a fourth encoded data slice (EDS 4_1), and the fifth row of X51-X54 corresponds to a fifth encoded data slice (EDS 5_1). Note that the second number of the EDS designation corresponds to the data segment number. In the illustrated example, the value X11=aD1+bD5+cD9, X12=aD2+bD6+cD10, . . . X53=mD3+nD7+oD11, and X54=mD4+nD8+oD12.

Returning to the discussion ofFIG. 3, the computing device also creates a slice name (SN) for each encoded data slice (EDS) in the set of encoded data slices. A typical format for aslice name80 is shown inFIG. 6. As shown, the slice name (SN)80 includes a pillar number of the encoded data slice (e.g., one of 1-T), a data segment number (e.g., one of 1-Y), a vault identifier (ID), a data object identifier (ID), and may further include revision level information of the encoded data slices. The slice name functions as at least part of a DSN address for the encoded data slice for storage and retrieval from theDSN memory22.

As a result of encoding, thecomputing device12 or16 produces a plurality of sets of encoded data slices, which are provided with their respective slice names to the storage units for storage. As shown, the first set of encoded data slices includes EDS 1_1 through EDS 5_1 and the first set of slice names includes SN 1_1 through SN 5_1 and the last set of encoded data slices includes EDS 1_Y through EDS 5_Y and the last set of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storage error decoding of a data object that was dispersed storage error encoded and stored in the example ofFIG. 4. In this example, thecomputing device12 or16 retrieves from the storage units at least the decode threshold number of encoded data slices per data segment. As a specific example, the computing device retrieves a read threshold number of encoded data slices.

In order to recover a data segment from a decode threshold number of encoded data slices, the computing device uses a decoding function as shown inFIG. 8. As shown, the decoding function is essentially an inverse of the encoding function ofFIG. 4. The coded matrix includes a decode threshold number of rows (e.g., three in this example) and the decoding matrix in an inversion of the encoding matrix that includes the corresponding rows of the coded matrix. For example, if the coded matrix includesrows1,2, and4, the encoding matrix is reduced torows1,2, and4, and then inverted to produce the decoding matrix.

FIG. 9 is a diagram of an example of a dispersed storage network. The dispersed storage network includes a DS (dispersed storage) client module34 (which may be inDS computing devices12 and/or16 ofFIG. 1), anetwork24, and a plurality of DS units36-1 . . .36-n(which may bestorage units36 ofFIG. 1 and which form at least a portion ofDS memory22 ofFIG. 1), a DSN managing unit (not shown—device18 inFIG. 1), and a DSintegrity verification module20. TheDS client module34 includes an outboundDS processing section81 and an inboundDS processing section82. Each of the DS units36-1 . . .36-nincludes acontroller86, aprocessing module84 including a communications interface for communicating over network24 (not shown),memory88, a DT (distributed task)execution module90, and aDS client module34.

In an example of operation, theDS client module34 receivesdata92. Thedata92 may be of any size and of any content, where, due to the size (e.g., greater than a few Terabytes), the content (e.g., secure data, etc.), and/or concerns over security and loss of data, distributed storage of the data is desired. For example, thedata92 may be one or more digital books, a copy of a company's emails, a large-scale Internet search, a video security file, one or more entertainment video files (e.g., television programs, movies, etc.), data files, and/or any other large amount of data (e.g., greater than a few Terabytes).

Within theDS client module34, the outboundDS processing section81 receives thedata92. The outboundDS processing section81 processes thedata92 to produceslice groupings96. As an example of such processing, the outboundDS processing section81 partitions thedata92 into a plurality of data partitions. For each data partition, the outboundDS processing section81 dispersed storage (DS) error encodes the data partition to produce encoded data slices and groups the encoded data slices into aslice grouping96.

The outboundDS processing section81 then sends, via thenetwork24, theslice groupings96 to the DS units36-1 . . .36-nof theDSN memory22 ofFIG. 1. For example, the outboundDS processing section81 sends slice group to DS storage unit36-1. As another example, the outboundDS processing section81 sends slice group #n to DS unit #n.

In one example of operation, theDS client module34 requests retrieval of stored data within the memory of theDS units36. In this example, thetask94 is retrieve data stored in theDSN memory22. Accordingly, and according to one embodiment, the outboundDS processing section81 converts thetask94 into a plurality ofpartial tasks98 and sends thepartial tasks98 to the respective DS storage units36-1 . . .36-n.

In response to thepartial task98 of retrieving stored data, aDS storage unit36 identifies the corresponding encoded data slices99 and retrieves them. For example,DS unit #1 receivespartial task #1 and retrieves, in response thereto, retrievedslices #1. TheDS units36 send their respective retrievedslices99 to the inboundDS processing section82 via thenetwork24.

The inboundDS processing section82 converts the retrieved slices99 intodata92. For example, the inboundDS processing section82 de-groups the retrieved slices99 to produce encoded slices per data partition. The inboundDS processing section82 then DS error decodes the encoded slices per data partition to produce data partitions. The inboundDS processing section82 de-partitions the data partitions to recapture thedata92.

FIG. 10A is a schematic block diagram of an embodiment of adecentralized agreement module350 that includes a set of deterministic functions340-1 . . .340-N, a set of normalizing functions342-1 . . .342-N, a set of scoring functions344-1 . . .344-N, and aranking function352. Each of the deterministic function, the normalizing function, the scoring function, and theranking function352, may be implemented utilizing theprocessing module84 ofFIG. 9. Thedecentralized agreement module350 may be implemented utilizing any module and/or unit of a dispersed storage network (DSN). For example, the decentralized agreement module is implemented utilizing the distributed storage (DS)client module34 ofFIG. 1.

Thedecentralized agreement module350 functions to receive a rankedscoring information request354 and to generate ranked scoringinformation358 based on the ranked scoringinformation request354 and other information. The rankedscoring information request354 includes one or more of an asset identifier (ID)356 of an asset associated with the request, an asset type indicator, one or more location identifiers of locations associated with the DSN, one or more corresponding location weights, and a requesting entity ID. The asset includes any portion of data associated with the DSN including one or more asset types including a data object, a data record, an encoded data slice, a data segment, a set of encoded data slices, and a plurality of sets of encoded data slices. As such, theasset ID356 of the asset includes one or more of a data name, a data record identifier, a source name, a slice name, and a plurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examples of locations include one or more of a storage unit, a memory device of the storage unit, a site, a storage pool of storage units, a pillar index associated with each encoded data slice of a set of encoded data slices generated by an information dispersal algorithm (IDA), aDS client module34 ofFIG. 1, a DS processing unit (computing device)16 ofFIG. 1, a DSintegrity processing unit20 ofFIG. 1, aDSN managing unit18 ofFIG. 1, a user device (computing device)12 ofFIG. 1, and a user device (computing device)14 ofFIG. 1.

Each location is associated with a location weight based on one or more of a resource prioritization of utilization scheme and physical configuration of the DSN. The location weight includes an arbitrary bias which adjusts a proportion of selections to an associated location such that a probability that an asset will be mapped to that location is equal to the location weight divided by a sum of all location weights for all locations of comparison. For example, each storage pool of a plurality of storage pools is associated with a location weight based on storage capacity. For instance, storage pools with more storage capacity are associated with higher location weights than others. The other information may include a set of location identifiers and a set of location weights associated with the set of location identifiers. For example, the other information includes location identifiers and location weights associated with a set of memory devices of a storage unit when the requesting entity utilizes thedecentralized agreement module350 to produce ranked scoringinformation358 with regards to selection of a memory device of the set of memory devices for accessing a particular encoded data slice (e.g., where the asset ID includes a slice name of the particular encoded data slice).

Thedecentralized agreement module350 outputs substantially identical ranked scoring information for each ranked scoring information request that includes substantially identical content of the ranked scoring information request. For example, a first requesting entity issues a first ranked scoring information request to thedecentralized agreement module350 and receives first ranked scoring information. A second requesting entity issues a second ranked scoring information request to the decentralized agreement module and receives second ranked scoring information. The second ranked scoring information is substantially the same as the first ranked scoring information when the second ranked scoring information request is substantially the same as the first ranked scoring information request.

As such, two or more requesting entities may utilize thedecentralized agreement module350 to determine substantially identical ranked scoring information. As a specific example, the first requesting entity selects a first storage pool of a plurality of storage pools for storing a set of encoded data slices utilizing thedecentralized agreement module350 and the second requesting entity identifies the first storage pool of the plurality of storage pools for retrieving the set of encoded data slices utilizing thedecentralized agreement module350.

In an example of operation, thedecentralized agreement module350 receives the ranked scoringinformation request354. Each deterministic function performs a deterministic function on a combination and/or concatenation (e.g., add, append, interleave) of theasset ID356 of the ranked scoringinformation request354 and an associated location ID of the set of location IDs to produce an interim result341-1 . . .341-N. The deterministic function includes at least one of a hashing function, a hash-based message authentication code function, a mask generating function, a cyclic redundancy code function, hashing module of a number of locations, consistent hashing, rendezvous hashing, and a sponge function. As a specific example, deterministic function340-2 appends a location ID339-2 of a storage pool to a source name as the asset ID to produce a combined value and performs the mask generating function on the combined value to produce interim result341-2.

With a set of interim results341-1 . . .341-N, each normalizing function342-1 . . .342N performs a normalizing function on a corresponding interim result to produce a corresponding normalized interim result. The performing of the normalizing function includes dividing the interim result by a number of possible permutations of the output of the deterministic function to produce the normalized interim result. For example, normalizing function342-2 performs the normalizing function on the interim result341-2 to produce a normalized interim result343-2.

With a set of normalized interim results343-1 . . .343-N, each scoring function performs a scoring function on a corresponding normalized interim result to produce a corresponding score. The performing of the scoring function includes dividing an associated location weight by a negative log of the normalized interim result. For example, scoring function344-2 divides location weight345-2 of the storage pool (e.g., associated with location ID339-2) by a negative log of the normalized interim result343-2 to produce a score346-2.

With a set of scores346-1 . . .346-N, theranking function352 performs a ranking function on the set of scores346-1 . . .346-N to generate the ranked scoringinformation358. The ranking function includes rank ordering each score with other scores of the set of scores346-1 . . .346-N, where a highest score is ranked first. As such, a location associated with the highest score may be considered a highest priority location for resource utilization (e.g., accessing, storing, retrieving, etc., the given asset of the request). Having generated the ranked scoringinformation358, thedecentralized agreement module350 outputs the ranked scoringinformation358 to the requesting entity.

FIG. 10B is a flowchart illustrating an example of selecting a resource. The method begins or continues atstep360 where a processing module (e.g., of a decentralized agreement module) receives a ranked scoring information request from a requesting entity with regards to a set of candidate resources. For each candidate resource, the method continues atstep362 where the processing module performs a deterministic function on a location identifier (ID) of the candidate resource and an asset ID of the ranked scoring information request to produce an interim result. As a specific example, the processing module combines the asset ID and the location ID of the candidate resource to produce a combined value and performs a hashing function on the combined value to produce the interim result.

For each interim result, the method continues atstep364 where the processing module performs a normalizing function on the interim result to produce a normalized interim result. As a specific example, the processing module obtains a permutation value associated with the deterministic function (e.g., maximum number of permutations of output of the deterministic function) and divides the interim result by the permutation value to produce the normalized interim result (e.g., with a value between 0 and 1).

For each normalized interim result, the method continues atstep366 where the processing module performs a scoring function on the normalized interim result utilizing a location weight associated with the candidate resource associated with the interim result to produce a score of a set of scores. As a specific example, the processing module divides the location weight by a negative log of the normalized interim result to produce the score.

The method continues atstep368 where the processing module rank orders the set of scores to produce ranked scoring information (e.g., ranking a highest value first). The method continues atstep370 where the processing module outputs the ranked scoring information to the requesting entity. The requesting entity may utilize the ranked scoring information to select one location of a plurality of locations.

FIG. 10C is a schematic block diagram of an embodiment of a dispersed storage network (DSN) that includes the distributed storage (DS) processing unit (computing device)16 ofFIG. 1, thenetwork24 ofFIG. 1, and the distributed storage network (DSN)module22 ofFIG. 1. Hereafter, theDSN module22 may be interchangeably referred to as a DSN memory. TheDS processing unit16 includes adecentralized agreement module380 and theDS client module34 ofFIG. 1. Thedecentralized agreement module380 being implemented utilizing thedecentralized agreement module350 ofFIG. 10A. TheDSN module22 includes a plurality of DS unit pools400-1 . . .400-N. Each DS unit pool includes one or more sites402-1 . . .402-N. Each site includes one or more DS units404-1-1 . . .404-1-N. Each DS unit may be associated with at least one pillar of N pillars associated with an information dispersal algorithm (IDA) (406-1 . . .406-N), where a data segment is dispersed storage error encoded using the IDA to produce one or more sets of encoded data slices, and where each set includes N encoded data slices and like encoded data slices (e.g.,slice3's) of two or more sets of encoded data slices are included in a common pillar (e.g., pillar406-3). Each site may not include every pillar and a given pillar may be implemented at more than one site. Each DS unit includes a plurality of memories (e.g. DS unit404-1-1 includes memories408-1-1-1 . . .408-1-1-N. Each DS unit may be implemented utilizing theDS unit36 ofFIG. 1 and thememories408 of DS units can be implemented utilizingmemory88 ofDS unit36 inFIG. 9. Hereafter, a DS unit may be referred to interchangeably as a storage unit and a set of DS units may be interchangeably referred to as a set of storage units and/or as a storage unit set.

The DSN functions to receivedata access requests382, select resources of at least one DS unit pool for data access, utilize the selected DS unit pool for the data access, and issue adata access response392 based on the data access. The selecting of the resources includes utilizing a decentralized agreement function of thedecentralized agreement module380, where a plurality of locations are ranked against each other. The selecting may include selecting one storage pool of the plurality of storage pools, selecting DS units at various sites of the plurality of sites, selecting a memory of the plurality of memories for each DS unit, and selecting combinations of memories, DS units, sites, pillars, and storage pools.

In an example of operation, theDS client module34 receives thedata access request382 from a requesting entity, where thedata access request382 includes at least one of a store data request, a retrieve data request, a delete data request, a data name, and a requesting entity identifier (ID). Having received thedata access request382, theDS client module34 determines a DSN address associated with the data access request. The DSN address includes at least one of a source name (e.g., including a vault ID and an object number associated with the data name), a data segment ID, a set of slice names, a plurality of sets of slice names. The determining includes at least one of generating (e.g., for the store data request) and retrieving (e.g., from a DSN directory, from a dispersed hierarchical index) based on the data name (e.g., for the retrieve data request).

Having determined the DSN address, theDS client module34 selects a plurality of resource levels (e.g., DS unit pool, site, DS unit, pillar, memory) associated with theDSN module22. The determining may be based on one or more of the data name, the requesting entity ID, a predetermination, a lookup, a DSN performance indicator, and interpreting an error message. For example, theDS client module34 selects the DS unit pool as a first resource level and a set of memory devices of a plurality of memory devices as a second resource level based on a system registry lookup for a vault associated with the requesting entity.

Having selected the plurality of resource levels, theDS client module34, for each resource level, issues a rankedscoring information request384 to thedecentralized agreement module380 utilizing the DSN address as an asset ID. Thedecentralized agreement module380 performs the decentralized agreement function based on the asset ID (e.g., the DSN address), identifiers of locations of the selected resource levels, and location weights of the locations to generate ranked scoringinformation386.

For each resource level, theDS client module34 receives corresponding ranked scoringinformation386. Having received the ranked scoringinformation386, theDS client module34 identifies one or more resources associated with the resource level based on therank scoring information386. For example, theDS client module34 identifies a DS unit pool associated with a highest score and identifies a set of memory devices within DS units of the identified DS unit pool with a highest score.

Having identified the one or more resources, theDS client module34 accesses theDSN module22 based on the identified one or more resources associated with each resource level. For example, theDS client module34 issues resource access requests388 (e.g., write slice requests when storing data, read slice requests when recovering data) to the identified DS unit pool, where theresource access requests388 further identify the identified set of memory devices. Having accessed theDSN module22, theDS client module34 receives resource access responses390 (e.g., write slice responses, read slice responses). TheDS client module34 issues thedata access response392 based on the receivedresource access responses390. For example, theDS client module34 decodes received encoded data slices to reproduce data and generates thedata access response392 to include the reproduced data.

FIG. 10D is a flowchart illustrating an example of accessing a dispersed storage network (DSN) memory. The method begins or continues atstep410 where a processing module (e.g., of a distributed storage (DS) client module) receives a data access request from a requesting entity. The data access request includes one or more of a storage request, a retrieval request, a requesting entity identifier, and a data identifier (ID). The method continues atstep412 where the processing module determines a DSN address associated with the data access request. For example, the processing module generates the DSN address for the storage request. As another example, the processing module performs a lookup for the retrieval request based on the data identifier.

The method continues atstep414 where the processing module selects a plurality of resource levels associated with the DSN memory. The selecting may be based on one or more of a predetermination, a range of weights associated with available resources, a resource performance level, and a resource performance requirement level. For each resource level, the method continues atstep416 where the processing module determines ranked scoring information. For example, the processing module issues a ranked scoring information request to a decentralized agreement module based on the DSN address and receives corresponding ranked scoring information for the resource level, where the decentralized agreement module performs a decentralized agreement protocol function on the DSN address using the associated resource identifiers and resource weights for the resource level to produce the ranked scoring information for the resource level.

For each resource level, the method continues atstep418 where the processing module selects one or more resources associated with the resource level based on the ranked scoring information. For example, the processing module selects a resource associated with a highest score when one resource is required. As another example, the processing module selects a plurality of resources associated with highest scores when a plurality of resources are required.

The method continues atstep420 where the processing module accesses the DSN memory utilizing the selected one or more resources for each of the plurality of resource levels. For example, the processing module identifies network addressing information based on the selected resources including one or more of a storage unit Internet protocol address and a memory device identifier, generates a set of encoded data slice access requests based on the data access request and the DSN address, and sends the set of encoded data slice access requests to the DSN memory utilizing the identified network addressing information.

The method continues atstep422 where the processing module issues a data access response to the requesting entity based on one or more resource access responses from the DSN memory. For example, the processing module issues a data storage status indicator when storing data. As another example, the processing module generates the data access response to include recovered data when retrieving data.

In one example of operation, the DSN ofFIG. 1 is grown to accommodate additional DS units. Further explanations of this process of deploying and growing a set of ds units at and by non-IDA width multiples are set out below in conjunction withFIGS. 11A and 11B. When DS units are deployed in a DSN memory with at least an IDA width number of DS units at a time, then maximum failure independence and accordingly, maximum reliability and availability are achieved. This set of DS units may be used to create or expand a storage pool for example. However, when fewer than an IDA width number of DS units are deployed, it is necessary that some DS units will store more than one slice for the same data source (e.g. when storing 15 slices across 5 DS units, each DS unit might store 3 slices each for the same data source). At some future time, it may become necessary to expand the DSN memory with more DS units. If the DSN memory was initially deployed with fewer than IDA width number of DS units then it may be desirable to use the additional DS units to more evenly distribute slices across a larger number of DS units, thereby improving reliability and availability. For example, two options exist for growing the initial deployment of 5 DS units when growing by an additional 5 DS units. Option 1: Treat each set of 5 DS units (each set) independently, and in a 15-wide continue storing 3 slices each to each DS unit and store all slices on either the first set of 5 DS units, or the second set of 5 DS units. Option 2: Use the existing set of 5 DS units, together with the new set of 5 DS units, to form a larger set containing 10 DS units, over which some no DS unit need to store more than 2 slices of the same source. The second option is preferable from a reliability and availability perspective.

To grow the system in this second way, the existing system expansion by reallocation via a Decentralized Agreement Protocol (DAP) can, according to one example, be used as follows:

1. Maintain the existing set of DS units as its own independent set in a storage pool;

2. Form a second set of DS units composed of the existing DS units together with the new DS units;

3. Initiate a reallocation of slices between these two sets, e.g. by setting the weight of the first set to “0” and the weight of the newly formed composite set equal to the size of the total number of DS units in the composite set;

4. Migrate slices from the smaller set to the larger set, moving slices to their new location in the new set within which each DS unit has a smaller fraction of the namespace; and

5. When the migration of all slices is complete, eliminate the original set of DS units, leaving behind only the new composite set.

In this way a set of DS units can be grown by as little as one DS unit at a time. However, once the set is grown to a size equal to 2*IDA width, it may make sense to “break” the large set into two smaller sets, each of size IDA width (set's in the sense of independent locations which slices may be mapped to by a Decentralized Agreement Protocol). Once the set is broken in this way, only the second set is grown, while the previous sets (each containing IDA width DS units) remain unchanged in the pool and is not expanded in this manner. The motivation for breaking off sets is it makes expanding the system by fewer than IDA width at a time more efficient. The fewer DS units in the set that is expanded in this way, the less total data transfer is required.

FIG. 11A is a schematic block diagram of another embodiment of a dispersed storage network (DSN) that includes the distributed storage (DS)processing unit16 ofFIG. 1, thenetwork24 ofFIG. 1, and at least two storage sets500-1 and500-2. The DS processing unit (computing device)16 includes theDS client module34 ofFIG. 1 and a decentralized agreement module. The decentralized agreement module may be implemented utilizing thedecentralized agreement module350 ofFIG. 10A. Each storage set includes a set of storage units36-nand may be expanded to accommodate increasing a storage capacity level of the storage set. For example, the storage set500-1 initially includes storage units36-1 to36-5 and is expanded to include storage units36-6 to36-10 to form the storage set500-2. Each storage unit may be implemented utilizing theDS units36 ofFIG. 1. The DSN functions to migrate data when the set of storage units is expanded.

In an example of operation of the migrating of data, theDS client module34 assigns one or more additional dispersed storage units to the storage set500-1 to form a new storage set500-2, where data is encoded utilizing a dispersed storage error encoding function in accordance with an information dispersal algorithm (IDA) width to produce a plurality of sets of encoded data slices that theDS processing unit16 stores in the storage set500-1 and where each set of encoded data slices includes an IDA width number of encoded data slices. For example, theDS processing unit16 stores three encoded data slices per storage unit of the storage units36-1 to36-5 when the IDA width is 15. The assigning of the one or more additional storage units includes one or more of determining a number of additional storage units, identifying available storage units, and selecting from the dispersed storage units identified for assignment by the middle storage units to produce the one or more additional storage units. The determining of the number of additional storage units to add may be based on one or more of estimated future storage requirements, an existing storage utilization level, and a predetermination.

For each encoded data slice stored in the existing storage set500-1, theDS client module34 utilizes a distributed agreement protocol function to select a storage unit of the new storage set500-2 for storage of an encoded data slice. This function may be implemented utilizing any module and/or unit of a dispersed storage network (DSN) including theDS Managing Unit18, theIntegrity Processing Unit20, and/or by one or more DS units36-1 . . .36-nshown inFIG. 1. For example, theDS client module34 utilizes the decentralized agreement module to perform the distributed agreement protocol function on a slice name associated with encoded data slice utilizing updated weights for each of the storage units of the existing storage set and newly established weights for each of the additional storage units to produce a score for each storage unit of the new storage set and identifies a storage unit associated with a highest score as the selected storage unit of the new storage set for storage of the encoded data slice.

Having selected the storage unit, theDS client module34 facilitates migration of the encoded data slice from the existing storage set500-1 to the selected storage unit of the new storage set500-2 when the encoded data slice is not presently stored in the selected storage unit. This could include migration to new DS units36-6 to36-10. For example, theDS client module34 receives, via thenetwork24, encoded data slices of storage set500-1 (502) that includes encoded data slice, and sends, via thenetwork24, encoded data slices of storage set500-2 (504) that includes the encoded data slice for migration, to the selected storage unit of the new storage set500-2 for storage.

FIG. 11B is a flowchart illustrating an example of migrating data. The method includes astep600 where a processing module of one or more processing modules of one or more computing devices (e.g., of a distributed storage (DS) client module) assigns one or more additional storage units to an existing storage set to form a new storage set of a dispersed storage network (DSN). The assigning includes one or more of determining a number of additional storage units (e.g., based on one or more of a predetermination, estimated future storage requirement, and existing storage utilization level), identifying available storage units, and selecting from the identified available storage units based on the number of additional storage units.

For each encoded data slice stored in existing storage set, the method continues at thestep602 where the processing module utilizes a distributed agreement protocol function to select a storage unit of the new storage set for storage of the encoded data slice. For example, the processing module performs the distributed agreement protocol function on a slice name associated with encoded data slice utilizing updated weights for the storage units of the existing storage set and newly established weights for the additional storage units of the new storage set to produce a score for each storage unit of the storage set and identifies a storage unit associated with a highest score of a plurality of scores as the selected storage unit.

The method continues at thestep604 where the processing module facilitates migration of encoded data slice from the existing storage set to the selected storage unit of the storage set when the encoded data slice is not presently stored within the selected storage unit. For example, the processing module retrieves encoded data slice from the existing storage set and sends the encoded data slice to the selected storage unit for storage.

The methods described above in conjunction with the computing device and the storage units can alternatively be performed by other modules of the dispersed storage network or by other devices. For example, any combination of a first module, a second module, a third module, a fourth module, etc. of the computing device and the storage units may perform the method described above. In addition, at least one memory section (e.g., a first memory section, a second memory section, a third memory section, a fourth memory section, a fifth memory section, a sixth memory section, etc. of a non-transitory computer readable storage medium) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices and/or by the storage units of the dispersed storage network (DSN), cause the one or more computing devices and/or the storage units to perform any or all of the method steps described above.

As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. Such an industry-accepted tolerance ranges from less than one percent to fifty percent. Such relativity between items ranges from a difference of a few percent to magnitude differences. As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”. As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., provides a desired relationship. For example, when the desired relationship is that signal A has a greater magnitude than signal B, a favorable comparison may be achieved when the magnitude of signal A is greater than that of signal B or when the magnitude of signal B is less than that of signal A. As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship.

As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof.

In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained.

The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from Figure to Figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones.

Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. The memory device may be in a form a solid state memory, a hard drive memory, cloud memory, thumb drive, server memory, computing device memory, and/or other physical medium for storing digital information. A computer readable memory/storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations.

Claims (20)

What is claimed is:

1. A method of growing a dispersed storage network, the dispersed storage network including a first set of dispersed storage units, wherein a first dispersed storage unit of the first set of dispersed storage units stores a first encoded data slice and a second encoded data slice and wherein the first encoded data slice and the second encoded data slice originate from a first data source, the method comprising:

assigning one or more additional dispersed storage units to the dispersed storage network including the first set of dispersed storage units to form a second set of dispersed storage units the second set of dispersed storage units including the first set of dispersed storage units and the one or more additional dispersed storage units;

reallocating the first encoded data slice from the first dispersed storage unit to at least one of the one or more additional dispersed storage units of the second set of dispersed storage units that does not presently store the first encoded data slice; and

facilitating migration of the first encoded data slice from the first dispersed storage unit to the at least one of the one or more additional dispersed storage units of the second set of dispersed storage units that does not presently store the first encoded data slice.

2. The method ofclaim 1, wherein the dispersed storage units in the first set of dispersed storage units are fewer than an information dispersal algorithm width number.

3. The method ofclaim 1, wherein assigning one or more additional dispersed storage units to the dispersed storage network comprises determining a number of additional dispersed storage units.

4. The method ofclaim 3, wherein assigning one or more additional dispersed storage units to the dispersed storage network comprises identifying the one or more additional dispersed storage units.

5. The method ofclaim 4, further comprising selecting the one or more additional dispersed storage units identified for assignment.

6. The method ofclaim 3, wherein determining the number of additional dispersed storage units is based on one or more of a predetermination, an estimated future storage requirements and existing storage utilization levels.

7. The method ofclaim 1, wherein assigning one or more additional dispersed storage units to the dispersed storage network uses a distributed agreement protocol.

8. The method ofclaim 7, wherein the distributed agreement protocol updates first weights for dispersed storage units of the first set of dispersed storage units and establishes second weights for the one or more additional dispersed storage units.

9. The method ofclaim 1, wherein facilitating migration comprises sending the first encoded data slice to a dispersed storage computing device.

10. A first dispersed storage unit of a first set of dispersed storage units for use in a dispersed storage network, the first dispersed storage unit comprising:

a communications interface;

a memory; and

a processor;

wherein the memory includes a first encoded data slice and a second encoded data wherein the first encoded data slice and the second encoded data slice originate from a first data source and wherein the memory further includes instructions for causing the processor to:

assign one or more additional dispersed storage units to the dispersed storage network including the first set of dispersed storage units to form a second set of dispersed storage units the second set of dispersed storage units including the first set of dispersed storage units and the one or more additional dispersed storage units;

reallocate the first encoded data slice from the first dispersed storage unit to at least one of the one or more additional dispersed storage units of the second set of dispersed storage units that does not presently store the first encoded data slice; and

facilitate migration of the first encoded data slice from the first dispersed storage unit to the at least one of the one or more additional dispersed storage units of the second set of dispersed storage units that does not presently store the first encoded data slice.

11. The first dispersed storage unit ofclaim 10, wherein the dispersed storage units in the first set of dispersed storage units are fewer than an information dispersal algorithm width number.

12. The first dispersed storage unit ofclaim 10, wherein the memory further comprises instructions for causing the processor to determine a number of additional dispersed storage units.

13. The first dispersed storage unit ofclaim 12, wherein the memory further comprises instructions for causing the processor identify the one or more additional dispersed storage units.

14. The first dispersed storage unit ofclaim 13, wherein the memory further comprises instructions for causing the processor to select the one or more additional dispersed storage units for assignment.

15. The first dispersed storage unit ofclaim 12, wherein the instructions for causing the processor to determine a number of additional dispersed storage units uses one or more of a predetermination, estimated future storage requirements and existing storage utilization levels.

16. The first dispersed storage unit ofclaim 10, wherein the instructions for causing the processor to assign one or more additional dispersed storage units to the dispersed storage network uses a distributed agreement protocol.

17. The first dispersed storage unit ofclaim 16, wherein the distributed agreement protocol is operable to update first weights for dispersed storage units of the first set of dispersed storage units and operable to establish second weights for the one or more additional dispersed storage units.

18. The first dispersed storage unit ofclaim 10, wherein the memory further comprises instructions for causing the processor to send the first encoded data slice to a dispersed storage computing device.

19. A dispersed storage network comprising:

a first set of dispersed storage units including a first dispersed storage unit;

the first dispersed storage unit including:

a communications interface;

a memory; and

a processor;

wherein the memory includes a first encoded data slice and a second encoded data wherein the first encoded data slice and the second encoded data slice originate from a first data source and wherein the memory further includes instructions for causing the processor to:

assign one or more additional dispersed storage units to the dispersed storage network including the first set of dispersed storage units to form a second set of dispersed storage units the second set of dispersed storage units including the first set of dispersed storage units and the one or more additional dispersed storage units;

reallocate the first encoded data slice from the first dispersed storage unit to at least one of the one or more additional dispersed storage units of the second set of dispersed storage units that does not presently store the first encoded data slice; and

facilitate migration of the first encoded data slice from the first dispersed storage unit to the at least one of the one or more additional dispersed storage units of the second set of dispersed storage units that does not presently store the first encoded data slice.

20. The dispersed storage network ofclaim 19, wherein the dispersed storage units in the first set of dispersed storage units are fewer than an information dispersal algorithm width number.

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